# BGB-16673 BTK Degrader PK/PD Simulator
# Based on: Wu et al. (2024) Br J Pharmacol 181:4973-4987
# DOI: 10.1111/bph.17332
# Translational PK/PD modeling for BTK-targeted protein degrader

library(shiny)
library(bslib)
library(mrgsolve)
library(dplyr)
library(ggplot2)

# ── mrgsolve Model Code ──
# Simplified mechanistic PK/PD model for targeted protein degraders (TPDs)
# Model: 2-compartment PK + BTK degradation dynamics

model_code <- '
$PARAM @annotated
// PK Parameters (from mouse studies, scaled for human)
CL    : 1.2    : Clearance (L/h)
V1    : 50     : Central volume (L)
V2    : 100    : Peripheral volume (L)
Q     : 5      : Intercompartmental CL (L/h)
KA    : 0.5    : Absorption rate constant (1/h)
F1    : 0.7    : Oral bioavailability

// Protein binding
FU    : 0.00017 : Unbound fraction (0.017% from paper)

// PD Parameters - BTK Degradation (Table 2 - blood, human in vivo predicted)
KOUT  : 0.017  : BTK turnover rate (1/h) - T1/2 ~41h
KDEG  : 1.0    : Proteasome-mediated degradation rate (1/h)
KAB   : 0.35   : Binding affinity TPD-BTK (nM) - in vivo scaled from 0.08nM in vitro

// Initial conditions
BTK0  : 100    : Baseline BTK (% of normal)

// Patient
WT    : 70     : Body weight (kg)

$CMT @annotated
DEPOT  : Oral depot (mg)
CENT   : Central compartment (mg)
PERIPH : Peripheral compartment (mg)
BTK    : BTK protein level (% of baseline)

$MAIN
// Allometric scaling
double CL_i = CL * pow(WT / 70.0, 0.75);
double V1_i = V1 * pow(WT / 70.0, 1.0);
double V2_i = V2 * pow(WT / 70.0, 1.0);
double Q_i  = Q  * pow(WT / 70.0, 0.75);

// Bioavailability for depot
F_DEPOT = F1;

// Initial BTK level
BTK_0 = BTK0;

$ODE
// Drug PK - 2-compartment with first-order absorption
double CP_total = CENT / V1_i;  // Total plasma conc (mg/L = ug/mL)
double CP_ngml  = CP_total * 1000.0;  // Convert to ng/mL
double CP_nM    = CP_ngml / 0.832;    // Convert to nM (MW ~832 g/mol estimated)
double CP_unbound_nM = CP_nM * FU;    // Unbound concentration in nM

dxdt_DEPOT  = -KA * DEPOT;
dxdt_CENT   = KA * DEPOT - (CL_i/V1_i + Q_i/V1_i) * CENT + (Q_i/V2_i) * PERIPH;
dxdt_PERIPH = (Q_i/V1_i) * CENT - (Q_i/V2_i) * PERIPH;

// BTK Degradation PD - Simplified mechanistic model
// d[Atotal]/dt = [A0]*kout - [Atotal]*kout - [AB]*kdeg
// where [AB] = [Atotal]*[B] / (KAB + [B]) (quasi-equilibrium)

double BTK_norm = BTK;  // BTK as % of baseline
double B = CP_unbound_nM;  // Unbound drug concentration (nM)

// Calculate bound fraction of BTK (quasi-equilibrium)
double frac_bound = B / (KAB + B);
double BTK_bound = BTK_norm * frac_bound;

// BTK turnover with degradation
// Synthesis (kin) = BTK0 * kout (maintains baseline at steady state)
double kin = BTK0 * KOUT;
dxdt_BTK = kin - BTK_norm * KOUT - BTK_bound * KDEG;

$TABLE
double CP_tot = CENT / V1_i;  // Total plasma (mg/L)
double CP_ng  = CP_tot * 1000.0;  // ng/mL
double CP_nM_out = CP_ng / 0.832;  // nM
double CP_unb_nM = CP_nM_out * FU;  // Unbound nM

// BTK outputs
double BTK_pct = BTK;  // BTK as % of baseline
double BTK_degradation = 100.0 - BTK;  // % degradation

// Clamp to valid ranges
BTK_pct = BTK_pct > 0 ? BTK_pct : 0;
BTK_pct = BTK_pct < 150 ? BTK_pct : 150;
CP_ng = CP_ng > 0 ? CP_ng : 0;

$CAPTURE @annotated
CP_tot      : Total plasma concentration (mg/L)
CP_ng       : Total plasma concentration (ng/mL)
CP_nM_out   : Total plasma concentration (nM)
CP_unb_nM   : Unbound plasma concentration (nM)
BTK_pct     : BTK protein level (% baseline)
BTK_degradation : BTK degradation (% from baseline)
'

# Compile model
mod <- mcode("bgb16673", model_code, compile = TRUE)

# ── Theme with custom metric cards ──
app_theme <- bs_theme(
  version = 5,
  bootswatch = "flatly",
  primary = "#8b5cf6"
) |> bs_add_rules("
  .metric-card {
    background: #f8f9fa;
    border-radius: 8px;
    padding: 15px;
    margin: 5px;
    text-align: center;
    border: 1px solid #dee2e6;
  }
  .metric-value {
    font-size: 24px;
    font-weight: bold;
    color: #2c3e50;
  }
  .metric-label {
    font-size: 12px;
    color: #7f8c8d;
  }
  .metric-success .metric-value { color: #10b981; }
  .metric-warning .metric-value { color: #f59e0b; }
  .metric-primary .metric-value { color: #8b5cf6; }
  .metric-info .metric-value { color: #0dcaf0; }
  .metric-danger .metric-value { color: #ef4444; }
")

# ── UI ──
ui <- page_sidebar(
  title = "BGB-16673 BTK Degrader PK/PD Simulator",
  theme = app_theme,
  
  sidebar = sidebar(
    title = "Simulation Settings",
    width = 340,
    
    h6("Patient Characteristics"),
    sliderInput("weight", "Body Weight (kg)", 
                min = 40, max = 120, value = 70, step = 5),
    
    hr(),
    h6("Dosing Regimen"),
    numericInput("dose", "Dose (mg)", value = 100, min = 10, max = 500, step = 10),
    selectInput("frequency", "Dosing Frequency",
                choices = c("Once daily (QD)" = "24",
                           "Twice daily (BID)" = "12",
                           "Once weekly (QW)" = "168"),
                selected = "24"),
    numericInput("n_doses", "Number of Doses", value = 7, min = 1, max = 28),
    sliderInput("sim_days", "Simulation Duration (days)", 
                min = 1, max = 56, value = 14),
    
    hr(),
    h6("Display Options"),
    checkboxInput("log_scale", "Log Scale (Y-axis)", value = FALSE),
    checkboxInput("show_unbound", "Show Unbound Concentration", value = TRUE),
    
    hr(),
    card(
      card_header("Model Parameters", class = "bg-light"),
      p(strong("BTK t½ (turnover):"), "41 h"),
      p(strong("KAB (BTK binding):"), "0.35 nM"),
      p(strong("kdeg' (degradation):"), "1.0 h⁻¹"),
      p(strong("fu (unbound):"), "0.017%"),
      p(em("Target: ≥90% BTK degradation"))
    ),
    
    hr(),
    p(style = "font-size: 10px; color: #7f8c8d;",
      "Based on: Wu et al. (2024) Br J Pharmacol 181:4973-4987")
  ),
  
  # Metrics row
  layout_columns(
    col_widths = c(3, 3, 3, 3),
    div(class = "metric-card metric-success",
      div(class = "metric-value", textOutput("cmax_val")),
      div(class = "metric-label", "Cmax (ng/mL)")
    ),
    div(class = "metric-card metric-warning",
      div(class = "metric-value", textOutput("ctrough_val")),
      div(class = "metric-label", "Ctrough (ng/mL)")
    ),
    div(class = "metric-card metric-danger",
      div(class = "metric-value", textOutput("btk_nadir")),
      div(class = "metric-label", "BTK Nadir (%)")
    ),
    div(class = "metric-card metric-primary",
      div(class = "metric-value", textOutput("max_degrad")),
      div(class = "metric-label", "Max Degradation (%)")
    )
  ),
  
  # Main plots
  layout_columns(
    col_widths = c(6, 6),
    
    card(
      card_header("Drug Pharmacokinetics"),
      full_screen = TRUE,
      plotOutput("pkPlot", height = "420px")
    ),
    
    card(
      card_header("BTK Protein Degradation"),
      full_screen = TRUE,
      plotOutput("pdPlot", height = "420px")
    )
  ),
  
  # Additional info tabs
  navset_card_tab(
    title = "Analysis",
    
    nav_panel("Dose-Response",
      plotOutput("doseResponsePlot", height = "420px")
    ),
    
    nav_panel("Model Information",
      markdown("
## BGB-16673 BTK Degrader

**Drug Class:** Targeted Protein Degrader (TPD) / PROTAC  
**Target:** Bruton's Tyrosine Kinase (BTK)  
**Developer:** BeiGene  
**Mechanism:** Cereblon (CRBN) E3 ligase-mediated proteasomal degradation

### Clinical Relevance

BTK is critical for B-cell receptor signaling and plays a key role in B-cell malignancies. While BTK inhibitors (ibrutinib, acalabrutinib, pirtobrutinib) have been successful, resistance mutations can develop. BGB-16673 is designed to degrade both wild-type and mutant BTK, potentially overcoming:

- **C481S mutation** (ibrutinib resistance)
- **T474I mutation** 
- **L528W mutation**

### Model Description

This simulator implements the simplified mechanistic PK/PD model from Wu et al. (2024):

**PK Model:** 2-compartment with first-order oral absorption  
**PD Model:** BTK turnover with degrader-mediated elimination

$$\\frac{d[BTK]}{dt} = k_{in} - [BTK] \\cdot k_{out} - [AB] \\cdot k_{deg}'$$

Where [AB] represents the drug-BTK binary complex formed in quasi-equilibrium.

### Key Parameters (Human Predicted)

| Parameter | Value | Description |
|-----------|-------|-------------|
| kout | 0.017 h⁻¹ | BTK turnover rate (t½ = 41 h) |
| KAB | 0.35 nM | Drug-BTK binding affinity (in vivo) |
| kdeg' | 1.0 h⁻¹ | Degradation rate constant |
| fu | 0.017% | Unbound fraction in plasma |

### References

1. Wu Y, et al. (2024) Br J Pharmacol 181:4973-4987 [DOI: 10.1111/bph.17332](https://doi.org/10.1111/bph.17332)
2. Wang H, et al. (2023) BGB-16673 preclinical characterization
      ")
    )
  ),

  # ── PKPDBuilder Branding Footer ──
  div(style = "text-align: center; padding: 20px; margin-top: 30px; 
      border-top: 1px solid #e9ecef; color: #6c757d; font-size: 12px;",
    "Powered by ",
    tags$a(href = "https://www.pkpdbuilder.com", target = "_blank", 
           style = "color: #8b5cf6; font-weight: 500;", "PKPDBuilder.com"),
    " • Built by Sunny ☀️ (Husain Attarwala's AI Assistant)",
    br(),
    tags$span(style = "font-size: 10px;", 
      "For research and educational purposes only. Not for clinical decision-making.")
  )
)

# ── Server ──
server <- function(input, output, session) {
  
  # Run simulation
  sim_data <- reactive({
    shiny::req(input$dose, input$n_doses, input$weight)
    
    # Parse frequency
    ii_hours <- as.numeric(input$frequency)
    sim_hours <- input$sim_days * 24
    
    # Create dosing events
    ev <- ev(amt = input$dose, cmt = 1, ii = ii_hours, addl = input$n_doses - 1)
    
    # Run simulation with patient weight
    out <- mod %>%
      param(WT = input$weight) %>%
      ev(ev) %>%
      mrgsim(end = sim_hours, delta = 0.5) %>%
      as.data.frame()
    
    out$time_days <- out$time / 24
    out
  })
  
  # Calculate metrics
  metrics <- reactive({
    d <- sim_data()
    shiny::req(nrow(d) > 0)
    
    # Get dosing interval for trough calculation
    ii_hours <- as.numeric(input$frequency)
    
    # Find Cmax (peak after any dose)
    cmax <- max(d$CP_ng, na.rm = TRUE)
    
    # Find Ctrough (just before next dose, after steady state ~5 doses)
    steady_start <- min(ii_hours * 5, max(d$time) * 0.5)
    d_ss <- d %>% dplyr::filter(time >= steady_start)
    ctrough <- if (nrow(d_ss) > 0) min(d_ss$CP_ng, na.rm = TRUE) else NA
    
    # BTK metrics
    btk_nadir <- min(d$BTK_pct, na.rm = TRUE)
    max_degradation <- max(d$BTK_degradation, na.rm = TRUE)
    
    list(
      cmax = cmax,
      ctrough = ctrough,
      btk_nadir = btk_nadir,
      max_degradation = max_degradation
    )
  })
  
  # Metric outputs
  output$cmax_val <- renderText({
    m <- metrics()
    sprintf("%.1f", m$cmax)
  })
  
  output$ctrough_val <- renderText({
    m <- metrics()
    if (is.na(m$ctrough)) return("--")
    sprintf("%.1f", m$ctrough)
  })
  
  output$btk_nadir <- renderText({
    m <- metrics()
    sprintf("%.1f", m$btk_nadir)
  })
  
  output$max_degrad <- renderText({
    m <- metrics()
    sprintf("%.0f%%", m$max_degradation)
  })
  
  # PK Plot
  output$pkPlot <- renderPlot({
    d <- sim_data()
    shiny::req(nrow(d) > 0)
    
    # Convert unbound nM to ng/mL for display (must be done BEFORE ggplot)
    d$CP_unb_ng <- d$CP_unb_nM * 0.832
    
    # Filter for log scale if needed
    if (input$log_scale) {
      d <- d %>% dplyr::filter(CP_ng > 0.01)
    }
    
    p <- ggplot(d, aes(x = time_days)) +
      theme_minimal(base_size = 14) +
      labs(x = "Time (days)", y = "Concentration (ng/mL)", color = "") +
      theme(legend.position = "bottom")
    
    if (input$show_unbound) {
      p <- p +
        geom_line(aes(y = CP_ng, color = "Total"), linewidth = 1.2) +
        geom_line(aes(y = CP_unb_ng * 1000, color = "Unbound (×1000)"), 
                  linewidth = 1, linetype = "dashed") +
        scale_color_manual(values = c("Total" = "#3b82f6", "Unbound (×1000)" = "#ef4444"))
    } else {
      p <- p +
        geom_line(aes(y = CP_ng), color = "#3b82f6", linewidth = 1.2)
    }
    
    if (input$log_scale) {
      p <- p + scale_y_log10()
    }
    
    p
  })
  
  # PD Plot (BTK)
  output$pdPlot <- renderPlot({
    d <- sim_data()
    shiny::req(nrow(d) > 0)
    
    # BTK degradation target
    target_90 <- 10  # 90% degradation = 10% remaining
    
    ggplot(d, aes(x = time_days, y = BTK_pct)) +
      geom_hline(yintercept = target_90, linetype = "dashed", color = "#ef4444", linewidth = 0.8) +
      geom_hline(yintercept = 100, linetype = "dotted", color = "#6b7280") +
      geom_line(color = "#10b981", linewidth = 1.5) +
      geom_ribbon(aes(ymin = 0, ymax = BTK_pct), fill = "#10b981", alpha = 0.2) +
      annotate("text", x = max(d$time_days) * 0.95, y = target_90 + 5, 
               label = "90% degradation", hjust = 1, size = 3.5, color = "#ef4444") +
      scale_y_continuous(limits = c(0, 110), breaks = seq(0, 100, 20)) +
      labs(x = "Time (days)", y = "BTK Protein (% of baseline)") +
      theme_minimal(base_size = 14)
  })
  
  # Dose-Response Plot
  output$doseResponsePlot <- renderPlot({
    shiny::req(input$weight)
    
    # Simulate multiple doses
    doses <- c(10, 25, 50, 100, 200, 300, 400, 500)
    ii_hours <- as.numeric(input$frequency)
    
    results <- lapply(doses, function(dose_mg) {
      ev <- ev(amt = dose_mg, cmt = 1, ii = ii_hours, addl = 6)  # 7 doses
      out <- mod %>%
        param(WT = input$weight) %>%
        ev(ev) %>%
        mrgsim(end = ii_hours * 7, delta = 1) %>%
        as.data.frame()
      
      data.frame(
        dose = dose_mg,
        btk_nadir = min(out$BTK_pct, na.rm = TRUE),
        cmax = max(out$CP_ng, na.rm = TRUE)
      )
    })
    
    dose_resp <- do.call(rbind, results)
    
    # Plot
    ggplot(dose_resp, aes(x = dose, y = btk_nadir)) +
      geom_hline(yintercept = 10, linetype = "dashed", color = "#ef4444") +
      geom_line(color = "#8b5cf6", linewidth = 1.5) +
      geom_point(size = 4, color = "#8b5cf6") +
      annotate("text", x = max(doses) * 0.9, y = 15, 
               label = "90% degradation target", hjust = 1, size = 3.5, color = "#ef4444") +
      scale_x_continuous(breaks = doses) +
      scale_y_continuous(limits = c(0, 100)) +
      labs(x = "Dose (mg)", y = "BTK Nadir (% of baseline)",
           title = paste0("BTK Degradation vs Dose (", input$frequency, "h dosing)")) +
      theme_minimal(base_size = 14)
  })
}

shinyApp(ui = ui, server = server)